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Zachary Armstrong

dblp:279/9944 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-4086-2946ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › phylogenetics
phylogenetic profiling
0.412020
TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler · Bioinform. 2020
Bioinformatics and computational biology › metagenomics
taxonomic classification
0.412020
TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler · Bioinform. 2020

Methods — techniques the papers use, named apart from their topics

linear regression of evolutionary distance on taxonomic rank · 0.4
YearPublicationVenuePosition
2020 TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler
abstract
MOTIVATION: Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. RESULTS: We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations. AVAILABILITY AND IMPLEMENTATION: TreeSAPP was developed in Python 3 as an open-source Python package and is available on GitHub at https://github.com/hallamlab/TreeSAPP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Connor Morgan-Lang, Ryan J. McLaughlin, Zachary Armstrong, Grace Zhang, Steven J. Hallam
Bioinform.3